Rémi Ounadjela - Data Science at TikTok, Google, Amazon & How to get into Big Tech #32
May 10, 2023
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Rémi Ounadjela shares his experience working on shipment optimization at Amazon and data science for risk and safety at TikTok. He discusses the differences between working at TikTok, Google, and Amazon. Rémi also gives advice on how to land a data science job in big tech and common mistakes to avoid.
Persistence and adaptability are crucial in transitioning to big tech roles like Amazon.
Building a robust data culture is essential for ensuring platform safety at TikTok.
Efficiency over perfection is key, with simple solutions often more valuable in business contexts.
Deep dives
Journey into Data Science and Learning Curve
Starting as a math and physics student, Remi stumbled into data science through internships during university. It all started with a failed interview at Goldman Sachs, leading to a machine learning internship at Schlimberg. This experience sparked his interest in data science, prompting him to pursue a Master's degree in the field.
Transition to Big Tech and Managing Ambiguity
Navigating into big tech companies like Amazon presented challenges for Remi, who faced rejections before securing an internship that later led to a full-time position. The transition highlighted the importance of persistence, adaptability, and seeking help when needed. The role at Amazon involved ownership of a part of the UK Supply Chain Network, showcasing the ambiguity and complexity typical of roles in big tech firms.
Trust and Safety at TikTok: Challenges and Solutions
Transitioning to TikTok as the Senior Data Science Manager in the Trust and Safety team posed new challenges related to content moderation at scale. Remi discusses the crucial role of AI in ensuring platform safety and highlights the complexities arising from imperfect data labeling. Addressing trust issues around metrics and building a robust data culture are essential for navigating the unique challenges faced in the social media landscape.
Finding Value in Approximate Models
It is highlighted that seeking complex and precise solutions is not always necessary, and having an approximate model that brings value to the business can be more efficient. The podcast discusses the importance of not getting lost in pursuing perfection, as it might hinder progress. Examples shared include the experience of switching focus from technical curiosity to business value at Amazon and understanding the effectiveness of simple solutions over more complex ones.
Navigating Differences Among Big Tech Companies
The episode delves into the differences and similarities between working at Amazon, Google, and TikTok. While each company offers unique experiences, the distinction lies in the level of establishment and opportunities for building from the ground up. Amazon is noted for its robust data culture and intense work pace, while TikTok provides exciting chances for career growth through pioneering projects. Cultural disparities, such as Amazon's emphasis on data independence and Google's polished tools, are also discussed, emphasizing the diverse environments in big tech companies.
Our guest today is Rémi Ounadjela, Senior Data Science Manager at TikTok and ex-Data Scientist at Google and Amazon.
During the first part of our conversation, Rémi talks about his experience working on shipment optimisation at Amazon and on Data Science for risk and safety at TikTok.
During the second part, we discuss the differences between working as a Data Scientist at TikTok, Google and Amazon. Rémi also shares advice on how to get into Big Tech and the common mistakes that you should avoid.
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